list_namespace_dependencies
Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph using only the flow IDs. Always return the legend after the graph.
This record as markdown: /tools/kestra-io-mcp-server-python/list-namespace-dependencies.md
What list_namespace_dependencies does on Kestra Python MCP Server
AI agents call list_namespace_dependencies to retrieve information from Kestra Python MCP Server without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why list_namespace_dependencies is rated Low
This tool queries and retrieves dependency information from a namespace and presents it in a rendered format. There are no side effects—it does not execute flows, modify configurations, delete data, or trigger external operations. It is a pure read operation that gathers and displays existing metadata about flow relationships.
From the tool's definition Tool name and description: 'Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph'.
Attacks that exploit this kind of access
The rule that runs list_namespace_dependencies safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Kestra Python MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For list_namespace_dependencies, this is the rule to start with:
list_namespace_dependencies is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Kestra Python MCP Server, apply this rule, and every list_namespace_dependencies call is checked against it from then on.
Questions about list_namespace_dependencies
Retrieve all flow-to-flow dependencies within a given namespace and render them as an ASCII dependency graph using only the flow IDs. Always return the legend after the graph. It is categorised as a Read tool in the Kestra Python MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for list_namespace_dependencies: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Kestra Python MCP Server. Nothing to install.
list_namespace_dependencies is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_namespace_dependencies rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for list_namespace_dependencies. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
list_namespace_dependencies is provided by the Kestra Python MCP Server MCP server (kestra-io/mcp-server-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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